Normalization and Batch Effect Correction (without doublets)
2021/01/29
1 Introduction
In previous notebooks and scripts, we excluded doublets and merged our Seurat object with the scRNA-seq dataset of tonsillar cells from Hamish et al.. Here, we will correct for batch effects with Harmony, and visualize the intermixing of different potential confounders pre- and post-integration.
In addition, we will save the dimensionality reduction (PCA) matrices before and after integration to further quantify the effect of the aforementioned confounders in high dimensional space.
2 Pre-processing
2.1 Load packages
library(Seurat)
library(SeuratWrappers)
library(harmony)
library(tidyverse)2.2 Parameters
# Paths
path_to_obj <- here::here("scRNA-seq/results/R_objects/seurat_merged_with_king_et_al.rds")
path_to_save_obj <- here::here("scRNA-seq/results/R_objects/seurat_merged_with_king_et_al_integrated.rds")
path_tmp_dir <- here::here("scRNA-seq/2-QC/5-batch_effect_correction/2-data_integration_king_et_al/tmp/")
path_to_save_dimred_uncorrect <- str_c(path_tmp_dir, "batch_uncorrected_pca.rds", sep = "")
path_to_save_dimred_correct <- str_c(path_tmp_dir, "batch_corrected_pca.rds", sep = "")
path_to_save_confounders_df <- str_c(path_tmp_dir, "confounders_df.rds", sep = "") 2.3 Load data
tonsil <- readRDS(path_to_obj)
tonsil## An object of class Seurat
## 29356 features across 299292 samples within 1 assay
## Active assay: RNA (29356 features, 0 variable features)3 Visualize UMAP without batch effect correction
# Process Seurat object
tonsil <- tonsil %>%
NormalizeData(normalization.method = "LogNormalize", scale.factor = 1e4) %>%
FindVariableFeatures(nfeatures = 3000) %>%
ScaleData() %>%
RunPCA() %>%
RunUMAP(reduction = "pca", dims = 1:30)
# Visualize UMAP
confounders <- c("library_name", "sex", "age_group", "is_hashed",
"hospital", "assay")
umaps_before_integration <- purrr::map(confounders, function(x) {
p <- DimPlot(tonsil, group.by = x, pt.size = 0.1)
p
})
names(umaps_before_integration) <- confounders
print("UMAP colored by GEM:")## [1] "UMAP colored by GEM:"umaps_before_integration$library_name + NoLegend()
print("UMAP colored by sex, age group, cell hashing status, sampling center and assay:")## [1] "UMAP colored by sex, age group, cell hashing status, sampling center and assay:"umaps_before_integration[2:length(umaps_before_integration)]## $sex
##
## $age_group
##
## $is_hashed
##
## $hospital
##
## $assay
4 Run and visualize Harmony’s integration
tonsil <- tonsil %>%
RunHarmony(reduction = "pca", dims = 1:30, group.by.vars = "gem_id") %>%
RunUMAP(reduction = "harmony", dims = 1:30)
umaps_after_integration <- purrr::map(confounders, function(x) {
p <- DimPlot(tonsil, group.by = x, pt.size = 0.1)
p
})
names(umaps_after_integration) <- confounders
print("UMAP colored by GEM:")## [1] "UMAP colored by GEM:"umaps_after_integration$library_name + NoLegend()
print("UMAP colored by sex, age group, cell hashing status, sampling center and assay:")## [1] "UMAP colored by sex, age group, cell hashing status, sampling center and assay:"umaps_after_integration[2:length(umaps_before_integration)]## $sex
##
## $age_group
##
## $is_hashed
##
## $hospital
##
## $assay
5 Save
# If it doesn't exist create temporal directory
dir.create(path_tmp_dir, showWarnings = FALSE)
# Save integrated Seurat object
saveRDS(tonsil, path_to_save_obj)
# Save PCA matrices to compute the Local Inverse Simpson Index (LISI)
confounders_df <- tonsil@meta.data[, confounders]
saveRDS(confounders_df, path_to_save_confounders_df)
saveRDS(
tonsil@reductions$pca@cell.embeddings[, 1:30],
path_to_save_dimred_uncorrect
)
saveRDS(
tonsil@reductions$harmony@cell.embeddings[, 1:30],
path_to_save_dimred_correct
)6 Session Information
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.7 (Santiago)
##
## Matrix products: default
## BLAS: /apps/R/3.6.0/lib64/R/lib/libRblas.so
## LAPACK: /home/devel/rmassoni/anaconda3/lib/libmkl_rt.so
##
## locale:
## [1] LC_CTYPE=C LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0 harmony_1.0 Rcpp_1.0.6 SeuratWrappers_0.2.0 Seurat_3.2.0 BiocStyle_2.14.4
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_1.4-1 deldir_0.1-25 ellipsis_0.3.1 ggridges_0.5.2 rprojroot_1.3-2 fs_1.4.1 rstudioapi_0.11 spatstat.data_1.4-3 farver_2.0.3 leiden_0.3.3 listenv_0.8.0 remotes_2.2.0 ggrepel_0.8.2 RSpectra_0.16-0 fansi_0.4.1 lubridate_1.7.8 xml2_1.3.2 codetools_0.2-16 splines_3.6.0 knitr_1.28 polyclip_1.10-0 jsonlite_1.7.2 broom_0.5.6 ica_1.0-2 cluster_2.1.0 dbplyr_1.4.4 png_0.1-7 uwot_0.1.8 shiny_1.4.0.2 sctransform_0.2.1 BiocManager_1.30.10 compiler_3.6.0 httr_1.4.2 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18 fastmap_1.0.1 lazyeval_0.2.2 cli_2.0.2 later_1.0.0 htmltools_0.4.0 tools_3.6.0 rsvd_1.0.3 igraph_1.2.5 gtable_0.3.0 glue_1.4.1 RANN_2.6.1 reshape2_1.4.4 rappdirs_0.3.1 spatstat_1.64-1 cellranger_1.1.0 vctrs_0.3.6 ape_5.3
## [55] nlme_3.1-148 lmtest_0.9-37 xfun_0.14 globals_0.12.5 rvest_0.3.5 mime_0.9 miniUI_0.1.1.1 lifecycle_0.2.0 irlba_2.3.3 goftest_1.2-2 future_1.17.0 MASS_7.3-51.6 zoo_1.8-8 scales_1.1.1 hms_0.5.3 promises_1.1.0 spatstat.utils_1.17-0 parallel_3.6.0 RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.16 pbapply_1.4-2 gridExtra_2.3 rpart_4.1-15 stringi_1.4.6 rlang_0.4.10 pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41 ROCR_1.0-11 tensor_1.5 labeling_0.3 patchwork_1.0.0 htmlwidgets_1.5.1 cowplot_1.0.0 tidyselect_1.1.0 here_0.1 RcppAnnoy_0.0.16 plyr_1.8.6 magrittr_1.5 bookdown_0.19 R6_2.4.1 generics_0.0.2 DBI_1.1.0 withr_2.4.1 pillar_1.4.4 haven_2.3.1 mgcv_1.8-31 fitdistrplus_1.1-1 survival_3.1-12 abind_1.4-5 future.apply_1.5.0 modelr_0.1.8 crayon_1.3.4
## [109] KernSmooth_2.23-17 plotly_4.9.2.1 rmarkdown_2.2 grid_3.6.0 readxl_1.3.1 data.table_1.12.8 blob_1.2.1 reprex_0.3.0 digest_0.6.20 xtable_1.8-4 httpuv_1.5.3.1 munsell_0.5.0 viridisLite_0.3.0